A robust pattern recognition-based fault detection and diagnosis (FDD) method for chillers

Abstract

A new chiller fault detection and diagnosis (FDD) method is proposed in this article. Different from conventional chiller FDD methods, this article considers the FDD problem as a typical one-class classification problem. The fault-free data are classified as the fault-free class. Data of a fault type are regarded as a fault class. The task of fault detection is to detect whether the process data are outliers of the fault-free class. The task of fault diagnosis is to find to which fault class does the process data belong. In this study, support vector data description (SVDD) algorithm is introduced for the one-class classification. The basic idea of the SVDD-based method is to find a minimum-volume hypersphere in a high dimensional feature space to enclose most of the data of an individual class. The proposed method is validated using the ASHRAE RP-1043 (Comstock and Braun 1999) experimental data. It shows more powerful FDD capacity than multi-class SVM-based FDD methods and PCA-based fault detection methods. Four potential applications of the proposed method are also discussed.